# Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths

arXiv: https://arxiv.org/abs/1912.12644 · HTML: https://mlsanity.com/paper/1912.12644/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Advanced Algorithms of Collision Free Navigation and Flocking for Autonomous UAVs](https://mlsanity.com/paper/2111.00166/) (2021)
- [RESC: A Reinforcement Learning Based Search-to-Control Framework for Quadrotor Local Planning in Dense Environments](https://mlsanity.com/paper/2408.00275/) (2024)
- [Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators](https://mlsanity.com/paper/2509.17381/) (2025)
- [Primitive-based Truncated Diffusion for Efficient Trajectory Generation of Differential Drive Mobile Manipulators](https://mlsanity.com/paper/2604.04166/) (2026)

## Abstract (arXiv)

Gradient-based trajectory optimization (GTO) has gained wide popularity for quadrotor trajectory replanning. However, it suffers from local minima, which is not only fatal to safety but also unfavorable for smooth navigation. In this paper, we propose a replanning method based on GTO addressing this issue systematically. A path-guided optimization (PGO) approach is devised to tackle infeasible local minima, which improves the replanning success rate significantly. A topological path searching algorithm is developed to capture a collection of distinct useful paths in 3-D environments, each of which then guides an independent trajectory optimization. It activates a more comprehensive exploration of the solution space and output superior replanned trajectories. Benchmark evaluation shows that our method outplays state-of-the-art methods regarding replanning success rate and optimality. Challenging experiments of aggressive autonomous flight are presented to demonstrate the robustness of our method. We will release our implementation as an open-source package.